Senior Data Scientist at The Heineken Company

• To lead the design, delivery, and governance of scalable, production-grade machine learning and LLM solutions that drive measurable business value. The role provides technical leadership, ensures best practice in MLOps and responsible AI, and mentors data scientists while aligning AI initiatives with business and enterprise standards.

Key Roles and Responsibilities

• Lead the translation of business requirements into technical delivery, ensuring alignment and effective execution across product and business teams.
• Lead or participate in the design, development, validation, and deployment of machine learning and LLM-based solutions.
• Define and enforce best practices for model validation, testing, monitoring, and governance, challenging existing ways of working where needed.
• Act as a technical leader and sparring partner, reviewing designs, challenging assumptions, and guiding technical decisions.
• Collaborate with data engineering and platform teams to ensure robust, scalable, and cost-effective ML and AI workloads.
• Communicate clearly with business and technical stakeholders about trade-offs, risks, and impact.
• Mentor other data scientists.

Qualifications and Experience:

• Master’s or PhD in Data Science, Computer Science, Statistics, or a related STEM field.
• Experience in data science and applied machine learning, with a track record of contributing to production ML solutions.
• Hands-on experience building and deploying LLM-powered applications, including retrieval-augmented architectures, agentic workflows, tool integration, and robust evaluation, monitoring, and optimization practices.
• Familiarity with responsible AI, model risk management, or regulated production environments.
• Proficient in Python, distributed data processing, and the use of Spark / Databricks.
• Proven experience deploying, scaling and maintaining production-grade ML systems in a cloud environment (preferably Azure).
• Strong understanding of MLOps best practices, including experiment tracking, model versioning, automated testing, CI/CD, and monitoring.
• Strong grounding in statistical thinking, model evaluation, and experimentation.

Closing Date: 09/05/2026